No abstract

No abstract

No abstract

Supplier selection is one of the most critical processes within the purchasing function. Choosing the right supplier makes a strategic difference to an organization’s ability to reduce costs and improve the quality of products by helping to select the most suitable supplier. Sütaş Dairy Company, which is entered to Macedonia market in 2012. In the dairy company, there is only one purchasing manager who selects the farmers. Importance weights of criteria are determined using his reference, and also the alternatives are evaluated according to each criterion. The most important criteria are product and other costs, the price is also playing an important role, but due to the small marketplace of Macedonia, the prices are almost the same in every region. To select the dairy supplier in Macedonia, Fuzzy-TOPSIS technique is used. The main goal of using fuzzy logic in this study is to help decision-makers for identifying the importance of selection criteria and rank possible suppliers easily. Since the supplier selection process is a Multi-Criteria Decision Making (MCDM) problem, after identify the weights and rankings in a fuzzy environment, TOPSIS algorithm has been used in the rest of the problem. Finally, fuzzy TOPSIS methodology has been implemented successfully, and its result pointed out the most suitable suppliers.

In recent years, environmental awareness has increased considerably, and in order to decrease endangerments such as air and water pollution, and also global warming, green procurement should be employed. Therefore, in the assessment of suppliers, their environmental performance should be taken into consideration along with other criteria for supplier selection. Raising awareness of sustainability in production and conservation and protection of the environment is very important both for the whole environment and for the company itself by increasing its competitive advantage. And, one of the steps to achieve this is for the companies to try to select green suppliers. So, the purpose of this study is to raise awareness and tackle the need for green supplier selection and, using multiple-criteria decision-making models, to elaborate a case study regarding this. A survey was conducted in a manufacturing firm. The data were analysed, and fuzzy MCDM (multicriteria decision-making) methods and artificial neural networks were implemented. Fuzzy methods are the fuzzy analytic hierarchy process (fuzzy AHP), fuzzy TOPSIS, and fuzzy ELECTRE. ANN supports the result of fuzzy MCDM models from the profit side. ANN can make the best estimate of the current year based on historical data. Fuzzy MCDM methods will also find good solutions using the available data but will produce different solutions as there are different decision-making methods. It is aimed to produce a synergy from the solutions obtained here and to produce a better solution. Instead of a single method, it would be more accurate to produce a better solution than the solution provided by all of them. The dominant result has been obtained using the committee fuzzy MCDM and ANN to select the best green supplier.

Abstract:When there is a production system with excess capacity, i.e., more capacity than the demand for the foreseeable future, upper management might consider utilizing only a portion of the available capacity by decreasing the number of workers or halting production on some of the machines/production lines, etc., while preserving the flexibility of the production system to satisfy demand spikes. To achieve this flexibility, upper management might be willing to attain some pre-determined/desired performance values in a production system having identical parallel machines in each work center. In this study, we propose a framework that utilizes parallel neural networks to make decisions on the availability of resources, due-date assignments for incoming orders, and dispatching rules for scheduling. This framework is applied to a flexible manufacturing system with work centers having parallel identical machines. The artificial neural networks were able to satisfactorily capture the underlying relationship between the design and control parameters of a manufacturing system and the resulting performance targets.

No abstract

This paper considers the problem of scheduling a given number of jobs on a specified number of identical parallel robots with unequal release dates and precedence constraints in order to minimize mean tardiness. This problem is strongly NP-hard. The author proposes a hybrid intelligent solution system, which uses Genetic Algorithms and Simulated Annealing (GA+SA). A genetic algorithm, as is well known, is an efficient tool for the solution of combinatorial optimization problems. Solutions for problems of different scales are found using genetic algorithms, simulated annealing and a Hybrid Intelligent Solution System (HISS). Computational results of empirical experiments show that the Hybrid Intelligent Solution System (HISS) is successful with regards to solution quality and computational time.